Joint Optimization Strategy of Task Migration and Power Allocation Based on Soft Actor-Critic in Unmanned Aerial Vehicle-Assisted Internet of Vehicles Environment DOI Creative Commons
Jingpan Bai,

Yifan Zhao,

Bo Yang

et al.

Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 693 - 693

Published: Nov. 20, 2024

In recent years, the unmanned aerial vehicle-assisted internet of vehicles has been extensively studied to enhance communication and computation services in vehicular environments where ground infrastructures are limited or absent. However, due limited-service range battery life vehicles, along with high mobility an vehicle cannot continuously cover serve same vehicle, leading interruptions application services. Therefore, this paper proposes a joint optimization strategy for task migration power allocation based on soft actor-critic (JOTMAP-SAC). First, models, computational resource models established sequentially dynamic coordinate each node. The problem is then formulated. Considering nature environment continuity action space, algorithm designed. This iteratively finds optimal solution problem, thereby reducing processing delay ensuring processing.

Language: Английский

Towards Efficient Task Offloading with Dependency Guarantees in Vehicular Edge Networks through Distributed Deep Reinforcement Learning DOI
Haoqiang Liu, Wenzhen Huang, Dong In Kim

et al.

IEEE Transactions on Vehicular Technology, Journal Year: 2024, Volume and Issue: 73(9), P. 13665 - 13681

Published: April 11, 2024

The proliferation of computation-intensive and delay-sensitive applications in the Internet Vehicles (IoV) poses great challenges to resource-constrained vehicles. To tackle this issue, Mobile Edge Computing (MEC) enabling offloading on-vehicle tasks edge servers has emerged as a promising approach. MEC jointly augments network computing capabilities alleviates resource utilization for IoV, garnering substantial attention. Nevertheless, efficacy depends heavily on adopted scheme, especially presence complex subtask dependencies. Existing research largely overlooked crucial dependencies among subtasks, which significantly influence decision making offloading. This work attempts schedule subtasks with guaranteed while minimizing system latency energy costs multi-vehicle scenarios. Firstly, we introduce priority scheduling method basis Directed Acyclic Graph (DAG) topological structure ensure order scenarios interdependencies. Secondly, light privacy concerns limited information sharing, propose an Optimized Distributed Computation Offloading (ODCO) scheme based deep reinforcement learning (DRL), alleviating conventional requirement extensive vehicle-specific sharing achieve optimal performance. adaptive $k$ -step approach is further presented enhance robustness training process. Numerical experiments are demonstrate advantages proposed regarding reduction cost and, more importantly, convergence rate comparison existing state-of-the-art schemes. For instance, ODCO achieved utility approximately 0.80 within 300 episodes, obtaining gains about 0.05 compared distributed earliest-finish time (DEFO) algorithm around 500 episodes.

Language: Английский

Citations

3
A hierarchical federated learning incentive mechanism in UAV-assisted edge computing environment DOI

Guangxuan He,

Chunlin Li, Mingyang Song

et al.

Ad Hoc Networks, Journal Year: 2023, Volume and Issue: 149, P. 103249 - 103249

Published: July 2, 2023

Language: Английский

Citations

7
Fine-grained access control policy in blockchain-enabled edge computing DOI

Guangxuan He,

Chunlin Li,

Yong Shu

et al.

Journal of Network and Computer Applications, Journal Year: 2023, Volume and Issue: 221, P. 103706 - 103706

Published: July 23, 2023

Language: Английский

Citations

7
Energy–latency tradeoffs edge server selection and DQN-based resource allocation schemes in MEC DOI
Chunlin Li,

Zewu Ke,

Qiang Liu

et al.

Wireless Networks, Journal Year: 2023, Volume and Issue: 29(8), P. 3637 - 3663

Published: June 30, 2023

Language: Английский

Citations

6
A GRL-aided federated graph reinforcement learning approach for enhanced file caching in mobile edge computing DOI

Abhinav Khanna,

G Anjali,

Nilesh Kumar Verma

et al.

Computing, Journal Year: 2024, Volume and Issue: 107(1)

Published: Dec. 30, 2024

Language: Английский

Citations

2
Low-latency AP handover protocol and heterogeneous resource scheduling in SDN-enabled edge computing DOI
Chunlin Li, Zhiqiang Yu, Xinyong Li

et al.

Wireless Networks, Journal Year: 2023, Volume and Issue: 29(5), P. 2171 - 2187

Published: March 10, 2023

Language: Английский

Citations

5
Primary node selection based on node reputation evaluation for PBFT in UAV-assisted MEC environment DOI
Yafeng Zhang,

Yongzheng Gan,

Chunlin Li

et al.

Wireless Networks, Journal Year: 2023, Volume and Issue: 29(8), P. 3515 - 3539

Published: June 21, 2023

Language: Английский

Citations

5
Efficient consensus algorithm based on improved DPoS in UAV-assisted mobile edge computing DOI
Mingyang Song, Chunlin Li, Jingsong Ye

et al.

Computer Communications, Journal Year: 2023, Volume and Issue: 207, P. 86 - 99

Published: May 11, 2023

Language: Английский

Citations

5
Integral reinforcement learning-based angular acceleration autopilot for high dynamic flight vehicles DOI
Y Liu, Yuhui Hu, Kai Shen

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 158, P. 111582 - 111582

Published: April 4, 2024

Language: Английский

Citations

1
An Efficient Multi-Edge Server Coalition Computation Offloading Scheme of Sensor-Edge-Cloud DOI Creative Commons
D. H.-L. Yin, Li Chen,

Jun Yang

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 12, P. 12909 - 12918

Published: Dec. 18, 2023

The high latency and energy consumption of wireless body areas networks (WBANs) for computing-intensive tasks is intolerable, especially remote interventional surgery. In this paper, a multi-mobile edge server collaborative computation offloading scheme proposed, which enables to choose offload certain proportion efficiently handle services massive users. More specifically, we formulate the problem minimizing system consumption, then model task resource allocation process as Markov decision (MDP). We have developed called m4m-PDQN optimize decisions, aiming minimize weighted sum consumption. Compared existing single-server schemes, it more effective in utilizing computing resources reducing waiting time multiple-server scenarios. experimental results show that outperforms other algorithms terms performance efficiency, significantly improving quality service (QoS) wearable area medical applications.

Language: Английский

Citations

1